Gesture recognition and communication

ABSTRACT

Systems and methods are described for facilitating non-verbal communication. An electronic processor receives data from the hand-gesture sensor indicative of one or more gestures performed by a user, determines at least one word or phrase corresponding to the data from the hand-gestures sensor, and outputs a text representation of the at least one word or phrase on the display screen. Some systems output the text representation as a natural language sentence based on the data from the hand-gesture sensor and linguistic prosody information determined based, at least in part, on image data of the user captured while performing the one or more gesture. Some systems also includes at least one brain sensor and generate an alternative natural language text sentence in response to detecting a signal from the brain sensor indicative of a contradiction response after displaying the natural language text sentence on the display screen.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 62/485,566, filed Apr. 14, 2017, and entitled “GESTURE RECOGNITION AND COMMUNICATION,” the entire contents of which are incorporated herein by reference.

BACKGROUND

The present invention relates to methods and systems for facilitating non-verbal communication.

SUMMARY

Non-verbal communication is a big part of day-to-day interactions. Body movements can be a powerful medium for non-verbal communication, which is done most effectively through gestures. However, the human-computer interfaces today are dominated by text based inputs and are increasingly moving towards voice-based control. Although speech is a very natural way to communication with other people and computers, it can be inappropriate in certain circumstances that require silence, or impossible in the case of deaf people.

In some embodiments, the system and methods described herein provide a common protocol for gesture-based communication and a framework that can successfully translate such communication gestures to meaningful information in real-time. In some embodiments, these systems and methods are pervasive and non-invasive.

In one embodiment, the invention provides a system for facilitating non-verbal communication. The system includes a hand-gesture sensor, a display screen, and an electronic processor. The electronic processor is configured to receive data from the hand-gesture sensor indicative of one or more gestures performed by a user. Based on the data from the hand-gesture sensor, the system determines at least one word or phrase corresponding to the data from the hand-gestures sensor and outputs a text representation of the at least one word or phrase on the display screen. In some embodiments, the system is further configured to output the text representation as a natural language sentence based on the data from the hand-gesture sensor and linguistic prosody information determined based, at least in part, on image data of the user captured while performing the one or more gesture. In some embodiments, the system also includes at least one brain sensor and generates an alternative natural language text sentence in response to detecting a signal from the brain sensor indicative of a contradiction response after displaying the natural language text sentence on the display screen.

In another embodiment, the invention provides a system for deciphering gesture-based communication. The system includes two-non-invasive wrist-worn devices and applies a multi-tiered template-based comparison system for classification to input data from an accelerometer, gyroscope, and electromyography sensors incorporated into the wrist-worn devices. In some embodiments, the system is trained to detect and identify various specific gestures including, for example, American Sign Language (ASL).

In yet another embodiment, the invention provides a system for deciphering gesture-based communication including a hand gesture sensor, a brain sensor, a camera, and an ear accelerometer. In some implementations, the hand gesture sensor includes at least one wrist-worn device that includes an accelerometer, a gyroscopic sensor, and an electromyography sensor. The system is configured to identify one or more hand gestures based at least in part on data received from the hand gesture sensors and generating a proposed output text based on the identified hand gestures and data from at least one additional sensor (e.g., the brain sensor, the camera, or the ear accelerometer). In some implementations, the system is configured to determine whether the proposed output text is to be generated as a statement, a question, or an exclamation based on the data from the at least one additional sensor.

In still other embodiments, the invention provides a system for deciphering and refining gesture-based communication including a hand gesture sensor and a brain sensor. The system is configured to identify one or more hand gestures based at least in part on data received from the hand gesture sensor, generates a first proposed output text, and displays the first proposed output text on a screen. While displaying the first proposed output text, the system monitors data from the brain sensor for a contradiction signal. In response to detecting the contradiction signal, the system generates a second proposed output text and displays the second proposed output text on the screen. In some embodiments, the system is configured to operate in a closed-loop repeatedly generating and displaying additional subsequent proposed output text until the contradiction signal is not detected in the data from the brain sensor while the proposed output text is displayed on the screen. In some embodiments, the system is configured to automatically transmit the proposed output text to a second user device in response to determining that the contradiction signal is not detected in the data from the brain sensor while the proposed output text is displayed on the screen.

Other aspects of the invention will become apparent by consideration of the detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS AND APPENDICES

FIG. 1 is a block diagram of a system for automated gesture recognition-based communication according to one embodiment.

FIG. 2 is a schematic flowchart of a method of communication by translating ASL gestures into text output using the system of FIG. 1.

FIG. 3 is a schematic flowchart of a method of communication by translating audio input into an animated video depiction of ASL gestures using the system of FIG. 1.

FIG. 4 is a schematic flowchart of a method of gesture recognition performed by the system of FIG. 1.

FIG. 5 is a block diagram of a system for automated gesture recognition-based communication enhanced by context and prosody information in accordance with another embodiment.

FIG. 6 is a schematic flowchart of a method for translating hand gestures into a language-based output using the system of FIG. 5.

FIG. 7 is a flowchart of an iterative closed-loop method for translating hand gestures into a language-based output based on sensed contradiction data using the system of FIG. 5.

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways.

FIG. 1 illustrates an example of a communication system configured to decipher gesture-based communication. The system in this example includes a portable device 101 such as, for example, a smart phone, tablet computer, a laptop computer, or other device. Although the example of FIG. 1 includes a portable device 101, in other implementations the functionality and hardware described in reference to the portable device 101 may be implemented in a non-portable device such as, for example, a desktop computer.

The portable device 101 includes an electronic processor 103 and a non-transitory, computer-readable memory 105. The memory 105 stores data and instructions that are executed by the electronic processor 103 to provide certain functionality of the portable device 101. The portable device 101 also includes a wireless transceiver 107 and a display 108. The portable device 101 is selectively coupleable to a series of sensors including a gyroscopic/orientation sensor 109, an accelerometer 111, and an EMG sensor 113. In some implementations, some of all of these sensors 109, 111, 113 are provided in a single wrist-worn device such as, for example, the “Myo” Armband from Thalmic Labs, Inc. Furthermore, although the example of FIG. 1 only shows one of each of the gyroscopic sensor 109, accelerometer 111, and EMG sensor 113, in some implementations the portable device 101 is selectively coupleable to two sets of sensors—one to be worn on each arm of the user. In some implementations, the sensors are selectively coupleable to the portable device by a wired or wireless communication link (e.g., Bluetooth, WiFi, etc.). Through the wireless transceiver 107, the portable device 101 is further configured to communicate with a remote server computer 115 and one or more additional user device (e.g., the second user device 117).

The system illustrated in the example of FIG. 1 can be used to facilitate gesture-based communication. For example, FIG. 2 illustrates one method of facilitating communication between a deaf person who is able to communicate using American Sign Language (ASL) and a hearing person who does not understand ASL using the system of FIG. 1. The deaf user wears the sensors illustrated in FIG. 1 and uses hand/arm gestures to communicate using ASL. The portable device 101 monitors data from the sensors and forwards a query based on the received gesture data to the remote server computer 115. The remote server computer 115 processes the gesture data to decipher the meaning of the ASL gestures and transmits a response to the portable device 101. The portable device 101 then outputs a text-based translation of the ASL gestures on the display 108. The deaf user can then communicate the message to the hearing user by showing the display 108 of the portable device 101 to the hearing user. Alternatively, the system might be configured to output the translated ASL message as audio through a speaker of the portable device 101 instead of or in addition to displaying the translated ASL message as text. In still other implementations, instead of outputting the translated ASL message as text or audio through the portable device 101, the system may be further configured to transmit the translated message to the second user device 117 (e.g., the smart phone or tablet computer of the hearing person).

In the example of FIG. 2, the processing of the sensor data is offloaded to the remote server computer 115. However, in other implementations, the system may be configured to perform some or all of the data processing locally on the electronic processor 103. Furthermore, in still other implementations in which the portable device 101 is implemented as a smart phone or tablet computer, the graphics processing unit (GPU) of the portable device can be utilized to parallelize the conversion method for faster execution.

The system of FIG. 1 can also be further adapted to provide for bi-directional communication between a hearing/speaking user and a user that communicates through ASL, for example, as illustrated in FIG. 3. In the example of FIG. 3, a hearing user enters a message into the second user device 117, for example, by speaking into a microphone of the second user device 117 or by typing the message as text into the second user device 117. The second user device 117—shown in the example of FIG. 3 as a smart phone—then transmits the message to the portable user device 101—shown in the example of FIG. 3 as a tablet computer. In some implementations where the message is entered by the hearing user as audio, the second user device 117 automatically converts the audio into text before transmitting the message to the portable user device 101. The portable user device 101 processes the received message and identifies ASL gestures corresponding to the message. Videos and/or images of the ASL gestures are then shown on the display 108 of the portable user device 101 where they are viewed by the deaf user.

Although the examples of FIGS. 2 and 3 illustrate particular situations in which the system of FIG. 1 is used to facilitate communication between a deaf user (using ASL) and a hearing user, in some implementations, the system of FIG. 1 is further adapted and configured to provide other communicative functions. For example, in some implementations, the system of FIG. 1 may be adapted to enable the user to draft and send text messages and/or emails to other users using ASL gestures instead of typing. Similarly, the system of FIG. 1 may be adapted to enable a user to draft and store notes or other text-based documents to the portable device using ASL gestures instead of typing.

The system of FIG. 1 may also be adapted to facilitate sign language communication between two users (e.g., between two deaf users) even if the two users communicate using different types of sign language. For example, the system might be configured to capture and translate the message from sign language into text using the method of FIG. 2 and, once translated into text, transmit and display the message in sign language on another portable device using the method of FIG. 3. In such implementations, the system can be configured to use natural language processing to represent meaning from a “sign”/gesture and to convert the meaning into a different sign language. Techniques such as lambda calculus can be used in this regard.

FIG. 4 illustrates in further detail an example of how the portable device 101 and/or the remote server computer 115 use gesture comparison and ranking to generate a text output from the data captured by the sensors. In this example, data is collected from a pair of wrist-worn devices while the user performs a gesture. Each wrist-worn device includes three accelerometers, three gyroscopic sensors, and eight EMG sensor “pods.” A gesture database is provided with gesture “training” information that is then compared to newly captured gesture data to identify a corresponding text output. The gesture database is trained, for example, by first selecting either a “guided mode” or “ASL mode.” A “sign” is selected from a drop down list or created through a user interface of the portable device 101 and then the user performs the gesture while wearing the wrist-worn devices. The system then annotates the input data with the name of the sign/gesture and stores the data to the gesture database.

If “guided mode” was selected, the system then performs a scan of the gesture database specific to the user and determines whether there are any clashes. If not, the user is asked to repeat the sign two more times after which the sign is stored to the gesture database and is ready to use. If, however, there is a clash (i.e., gesture data for the sign is already stored in the data base), then the user is instructed—through the user interface of the portable device 101, to choose another sign instead. If “ASL” mode was selected, the system does not give such feedback and simply prompts the user to train the system two more times.

After a trained gesture database is provided, the system can be used to identify gestures. A user wearing the wrist-worn devices performs a gesture and, as soon as the end of the gesture is detected or signaled, the preprocessing begins. The data collected from two hands is aggregated into one data-table and then stored into a file as an array of time-series data. At a 50 Hz sampling rate, a five second gesture will consist of six accelerometer vectors (each with a length of 250), six gyroscope vectors (each with a length of 250), and 16 EMG vectors (each with a length of 250). This data is combined into a 34×250 matrix. Each time-series is transformed to make sure the initial value is zero by subtracting this value from all values in the time-series. This helps to prevent errors when the user performs the sign/gesture with a different starting position. Normalization is then done by representing all values as floats between zero and one by a mix-max method.

Orientation values are received in the form of three time-series in terms of unit quaternions. The pitch, yaw, and roll values are obtained from the quaternion values w, x, y, and z by using the equation:

$\begin{matrix} {{{roll} = {\tan^{- 1}\left( \frac{2\left( {{wx} + {yz}} \right)}{{- x^{2}}y^{2}} \right)}}{{pitch} = {\sin^{- 1}\left( {\max\left( {{- 1},{\min\left( {1,{2\left( {{wy} - {zx}} \right)}} \right)}} \right)} \right)}}{{yaw} = {{\tan^{- 1}\left( \frac{2\left( {{wz} + {xy}} \right)}{{- y^{2}}x^{2}} \right)}.}}} & (1) \end{matrix}$

After correctly identifying the location of each of the individual pods of the two wrist-worn devices, data is stored and shuffled in such a way that the final stored data is aligned from EMG pod-1 to EMG pod-8. This provides flexibility as the user does not need to wear the wrist-worn devices in the same orientation/position every time. EMG energy, E, on each pod is calculated as the sum of squares of x[n], the value of the time-series at point ‘n’: E=sum(x[n]²).

Four different approaches are described herein for comparing the accelerometer and orientation data: (a) Euclidian distance, (b) Regression, (c) Principal Component Analysis (PCA), and (d) dynamic time warping (DTW). The Euclidian distance approach compares the two time-series using mean-squared error. Regression analysis fits a model to the time-series and uses this model to compare best fit for the test gesture. Given a set of features from the time-series for a gesture, PCA derives the optimal set of features for comparison. DTW is a technique to find an optimal alignment between two given (time-dependent) sequences under certain restrictions. Traditionally, DTW has been used extensively for speech recognition, and it is finding increasing use in the fields of gesture recognition as well, especially when combined with Hidden Markov Models. The example of FIG. 4 takes a simpler approach by randomly re-sampling the training and test datasets based on the least number of points in either one and then performing a DTW-based distance analysis on them. First a DTW analysis of Accelerometer Data is run and a ranked list of ‘probable’ signs is passed on for DTW based analysis of orientation Data which in turn creates an even shorter ranked list to be processed by the final EMG algorithm.

On another approach, the normalized distances from each of the outputs are taken and the sum of squares of the final output is taken as an indication of ‘closeness’ of a test sign to a training sign. Because this simplified approach is less computationally complex, it can improve the speed of gesture recognition.

The overall ‘nearness’ of two signs is computed to be the total distance between those signs which is obtained by adding up the scaled distances for accelerometer, orientation and EMG as discussed above. An extra step of scaling the distance values between (0,1) is performed so as to give equal weight to each of the features. Also, since we have 8 EMG pods and only 3 each of accelerometer and orientation sensors, we use the following formula for the combination. The formula is for combining accelerometer sum of distances and EMG sum of distances. Similar techniques were applied for the other combinations. An algorithmic summary is provided by the equation: dist=(8cs(sc_accl_comb)+3cs(sc_emg_comb))/24  (3) where cs( ) is a function that returns the sum of columns, sc_accl_comb is a data frame that holds the combined accelerometer DTW distances for both hands for all trained signs, and sc_emg_comb is a data frame that holds the combined EMG energy distances for both hands for all trained signs.

Due to timing constraints with respect to the real-time nature of the application, the recognition algorithm is optimized to be efficient, especially as the gesture space increases. As the number of gestures in the database increases to beyond 60, the recognition time for identifying one gesture goes beyond the 0.5 s mark. Thus, a comparison algorithm is configured to first compare to one stored instance of each gesture, then choose the top ‘n’ number of gestures which when compared to ‘k’ of each, still allowing the time-constraint to be fulfilled. We then, proceed with the normal gesture comparison routine on only these gesture instances and thus keep the recognition time within defined timing constraints. All the variables for this method viz. the ‘n’, ‘k’ are calculated dynamically by what is allowed by the timing constraint ‘tc’, thus making this approach fluid and adaptable to more vigorous time constraints if required.

The system, such as described in the various examples above, can be specifically trained and adapted to facilitate gesture-based communication in medical situations—particularly during urgent or emergency medical situations. Members of the deaf and hard of hearing community are at increased risk for misdiagnosis or delayed treatment in an urgent medical situation when they cannot quickly and accurately communicate their symptoms to healthcare providers even if they are fluent in American Sign Language (ASL). Equal access to quality healthcare can improve social functioning of the 2.1% and 13% of U.S. population who are deaf or hard-of-hearing, respectively. Communication barriers between healthcare providers and patients can significantly impact the quality of healthcare access. The consequences are most serious in emergency medical situations where information must be conveyed quickly and accurately. A recent survey on 89 deaf American Sign Language (ASL) users revealed that access to ASL interpretation can directly facilitate communication of important health information to the deaf patients and increase the appropriate use of preventative services. Increased availability of ASL interpretation might also improve communication efficiency in urgent medical situations. Hospital emergency rooms and urgent care facilities very rarely have in-person ASL interpreters consistently available. As a result, in absence of an ASL interpreter, communication with the patient will likely depend on note writing, which may be impeded by medical conditions, or ASL interpreting via remote video, which is costly.

The systems illustrated above are not only capable of facilitating gesture-based communication using a standardized sign language such as ASL, the system is also adapted to enable a user to train non-ASL gestures and associate them with specific concepts. This aspect is particularly relevant for health-related conditions because many medical terms may not have standard “signs” in ASL and, therefore, must be fingerspelled. Given that medical terms are often long and can be arduous to fingerspell, the systems and methods described above can greatly increase the speed and ease of communication by allowing the deaf patient to use a single gesture to convey a medical term.

In addition to complications introduced by attempting to use gesture-based communication to communicate terms for which there is no corresponding “sign,” automated interpretation and capture of gesture-based communication can be further complicated by a lack of gestures to represent inflectional bound morphemes (e.g., suffixes indicating tense or degree) and linguistic prosody (e.g., indicating a statement vs. a question). Instead, users of ASL may express these components of the communication through other cues including eyebrow/body movements and facial expressions. Depending on these different sign cues, the person may be asking a question, expressing surprise, or neutrally expressing a statement.

Accordingly, the systems and methods discussed above can be further adapted in some implementations to extract linguistic and affective prosody from signed communication and incorporating that prosodic information into an appropriate spoken language translation. FIG. 5 illustrates an example of a system that, like the example of FIG. 1 discussed above, includes a portable device 501 with an electronic processor 503, a non-transitory computer-readable memory 505, a wireless transceiver 507, and a display 508. The portable device 501 is selectively coupleable (e.g., through a wired or wireless communication link) to a series of sensors including one or more hand gesture sensors 509, a camera 511, an ear accelerometer 513, and one or more brain sensors 515. In some implementations, the hand gesture sensors 509 are again provided as a pair of wrist-worn devices that each include a plurality of acceleration, orientation/gyroscopic, and EMG sensors (e.g., the “Myo” Armband). The portable device 501 is also configured to communicate with other devices through its wireless transceiver 507 including, for example, a remote server computer 517 and a second user device 519.

Video data captured by the camera 511 (e.g., the built-in camera of a smart phone) can provide some potentially prosodic cues from head movement and facial expressions. However, the use of image data alone may be limited in that the signer may display eyebrow and body movements or facial expressions that are not part of the communicative message. Accordingly, for these potentially prosodic cues to be useful, the system must be further configured to determine whether the body/facial movements are related to the signed communication and when they are not.

Brain activity sensed by the one or more brain sensors 515 can also provide information that can be used for processing prosodic factors. For example, an expression of prosody information in spoken language is often preceded by a positive CZ channel response (called P300) and a prosody contradiction can be indicated by a unique negativity in the CZ channel (called N300) followed by a characteristic slow wave response. Accordingly, the system is configured to monitor the output from the brain sensors 515 to identify a cognitive connection between the modalities of hands, eyes, and body movements. For example, in some implementations, the system is configured to detect the P300 channel response and, in response, to flag certain movements and facial expressions that might be detected in the captured image data within a defined time period after the detection of the P300 channel response as potentially prosodic.

FIG. 6 illustrates an example of a method of incorporating brain activity into sign language interpretation using the system of FIG. 5. The system monitors eyebrow and movements along with hand gestures through the wearable sensors and will also monitor the brain for emotional responses (e.g., a highly positive CZ channel data also known as the P300 response) from the signer. From the monitored brain data, the system will make a distinction between statement, question, and exclamation. The system then uses an appropriate ASL syntax to determine the sequence of signs performed by the individual. Based on the intensity of the brain activity during the signed movements, the system will choose appropriate implied modifiers. Using a model based on English language, the signed sequence will then be translated into an English sentence with prosody.

In some implementations, the system is further configured to implement closed-loop feedback to iteratively revise the English language sentence until it matches the intended message of the signer. In the example of FIG. 7, the system receives the hand gesture data (at block 701) and identifies the ASL gesture (at block 703). The system also analyzes facial, body, and brain data to determine syntax, context, and prosodic information (at block 705) before constructing a textual sentence (at block 707). The constructed textual sentence is then displayed to the user (e.g., on the display 508 of the portable device 501) (at block 709). The system then searches the data from the one or more brain sensors for an N300 response—the response associated with prosody contradiction (at block 711). A contradiction response would signal an incorrect interpretation and, in response, the system would then process alternative interpretations (e.g., the inclusion of a different modifier or a change in linguistic prosody) and select a new sentence for output on the display 508 (i.e., repeating steps 705, 707, and 709). This closed loop feedback operation continues until the person is satisfied with the translation (i.e., no N300 response detected).

In the examples of FIGS. 5 through 7, the system is configured for four types of monitoring: (1) brain activity for affective prosody extraction, (2) eyebrow movement for linguistic prosody extraction, (3) body movement for linguistic prosody extraction, and (4) hand gesture for extraction of ASL signs. In various other implementations, more, fewer, or different signals can be monitored for the functional purpose described herein. Similarly, more, fewer, or different sensors can be utilized. However, in this specific example, hand gestures are monitored using the “Myo Armband” including accelerometers, gyroscopic sensors, and eight electromyogram (EMG) pods and facial expressions are monitored using the ear accelerometer (detecting facial expressions that cause corresponding movements at the ear) and the camera data. In this example, brain activity can be monitored using either (a) a multi-channel EEG recording device (e.g., the ABM B-Alert X10 EEG) or (b) a single channel device (e.g., the Neurosky device). A multi-channel EEF device provides greater accuracy, but the single channel device provides better wearability for use in real-time situations.

Thus, the invention provides, among other things, a system and method for facilitating gesture-based communication and, in some implementations, for translating gesture data into natural language sentences accounting for prosody. Various features and advantages of the invention are set forth in the following claims. 

What is claimed is:
 1. A system for facilitating non-verbal communication, the system comprising: a hand-gesture sensor; at least one brain signal sensor; a display screen; and an electronic processor configured to receive data from the hand-gesture sensor indicative of one or more gestures performed by a user, determine, based on the data from the hand-gesture sensor, at least one word or phrase corresponding to the data from the hand-gesture sensor, and output on the display screen a text representation of the at least one word or phrase, monitor the at least one brain signal sensor for a signal indicative of a contradiction response, and in response to detecting the signal indicative of the contradiction response after outputting the text representation of the at least one word or phrase on the display screen, determining an alternative word or phrase based at least in part on the data from the hand-gesture sensor and outputting a text representation of the alternative word or phrase on the display screen.
 2. The system of claim 1, wherein the hand-gesture sensor includes at least one sensor from a group consisting of a gyroscopic sensor, an accelerometer, and an EMG sensor, wherein the data received from the hand-gesture sensor includes a time-series data array of measured values from the at least one sensor sampled at a defined sampling rate, and wherein at least one word or phrase or the text representation of the at least one word or phrase is determined based in part on an output of the at least one brain sensor.
 3. The system of claim 2, further comprising a computer-readable memory storing a gesture database, wherein the gesture database stores data indicative of a plurality of unique time-series data arrays and defines a different word or phrase corresponding to each unique time-series data array of the plurality of time-series data arrays, and wherein the electronic processor is configured to determine the at least one word or phrase corresponding to the data from the hand-gesture sensor by identifying a unique time-series data array of the plurality of unique time-series data arrays stored on the computer-readable memory that most closely matches the data from the hand-gesture sensor.
 4. The system of claim 3, wherein the electronic processor is further configured to selectively operate in a training mode, output a prompt on the display screen, while operating in the training mode, instructing the user to perform a gesture corresponding to a particular word or phrase, receive the data from the hand-gesture sensor after outputting the prompt, and update the gesture database to define a new unique time-series data array corresponding to the particular word or phrase based on the data received from the hand-gesture sensor after outputting the prompt.
 5. The system of claim 4, wherein the electronic processor is configured to output a prompt on the display screen instructing the user to perform an American Sign Language (ASL) gesture corresponding to the particular word or phrase.
 6. The system of claim 5, wherein the electronic processor is configured to train the gesture database by iteratively prompting the user to perform a different one of a series of ASL gestures while receiving data from the hand-gesture sensor after each prompt.
 7. The system of claim 1, wherein the hand-gesture sensor includes a plurality of gyroscopic sensors, a plurality of accelerometers, and a plurality of EMG sensors incorporated into a wrist-worn housing, and wherein the data received from the hand-gesture sensor includes a time-series data array of measured values from each gyroscopic sensor of the plurality of gyroscopic sensors, each accelerometer of the plurality of accelerometers, and each EMG sensor of the plurality of EMG sensors.
 8. The system of claim 1, wherein the electronic processor is further configured to receive audio stream data, determine a particular word or phrase corresponding to the audio stream data, and output a video on the display screen of a gesture corresponding to the particular word or phrase.
 9. The system of claim 1, further comprising: a first portable user device including the display screen, the electronic processor, and a wireless transceiver; and a remote server computer, wherein the remote server computer is configured to receive, from the first portable user device, data indicative of the data received from the hand-gesture sensor, process the data from the first portable user device to identify the at least one word or phrase, and transmit to the first portable user device an indication of the identified at least one word or phrase.
 10. A system for facilitating non-verbal communication, the system comprising: a hand-gesture sensor; a display screen; a camera; and an electronic processor configured to receive data from the hand-gesture sensor indicative of one or more gestures performed by a user, determine, based on the data from the hand-gesture sensor, at least one word or phrase corresponding to the data from the hand-gesture sensor, and output on the display screen a text representation of the at least one word or phrase, receive image data of the user from the camera, determine, based on the image data from the camera, a linguistic prosody of the user, and generate a text sentence based on the at least one word or phrase corresponding to one or more hand gestures determined based on the data received from the hand-gesture sensor and the determined linguistic prosody of the user.
 11. The system of claim 10, wherein the electronic processor is further configured to determine the linguistic prosody of the user by detecting, in the image data, at least one expression or movement indicative of a question, detecting, in the image data, at least one expression or movement indicative of an exclamation, and detecting, in the image data, at least one expression or movement indicative of a neutral statement, and wherein the electronic processor is configured to generate the text sentence based on the at least one word or phrase corresponding to one or more hand gestures determined based on the data received from the hand-gesture sensor and the determined linguistic prosody of the user by generating the text sentence as a question in response to detecting, in the image data, the at least one expression or movement indicative of the question, generating the text sentence as an exclamation in response to detecting, in the image data, the at least one expression or movement indicative of the exclamation, and generating the text sentence as a neutral statement in response to detecting, in the image data, the at least one expression or movement indicative of the neutral statement.
 12. The system of claim 10, wherein the image data of the user from the camera includes at least one selected from a group consisting of facial movements of the user, facial expressions of the user, and body movements of the user.
 13. The system of claim 10, further comprising at least one brain signal sensor, wherein the electronic processor is further configured to monitor the at least one brain signal sensor for a signal indicative of an expression of prosody information, and wherein the electronic processor is configured to analyze the image data from the camera to determine, based on the image data from the camera, a linguistic prosody of the user in response to detecting the signal indicative of the expression of prosody information.
 14. The system of claim 13, wherein the electronic processor is configured to monitor the at least one brain signal sensor for the signal indicative of the expression of prosody information by detecting a positive CZ channel response in data from the at least one brain signal sensor.
 15. The system of claim 13, wherein the electronic processor is further configured to monitor the at least one brain signal sensor for a signal indicative of a contradiction response after outputting the text sentence on the display screen, in response to detecting the signal indicative of the contradiction response after outputting the text sentence on the display screen, generating an alternative text sentence based at least in part on the data received from the hand-gesture sensor and the determined linguistic prosody of the user based on the image data from the camera, and output the alternative text sentence on the display screen.
 16. The system of claim 15, wherein the electronic processor is configured to generate the alternative text sentence by generating the alternative text sentence based on an alternative linguistic prosody or an alternative word or phrase corresponding to at least one gesture.
 17. The system of claim 15, wherein the electronic processor is configured to generate the text sentence in the form of a question in response to detecting, in the image data, at least one expression or movement indicative of a question, and generate the alternative text sentence in the form of a neutral statement in response to detecting, in the image data, the at least one expression of movement indicative of the question and subsequently detecting the signal indicative of the contradiction response after outputting the text sentence on the display screen in the form of the question.
 18. The system of claim 15, wherein the signal indicative of the contradiction response includes a negative CZ channel response followed by a slow wave response.
 19. A system for facilitating non-verbal communication, the system comprising: at least one brain sensor; a hand-gesture sensor, wherein the hand-gesture sensor includes at least one sensor from a group consisting of a gyroscopic sensor, an accelerometer, and an EMG sensor, wherein the data received from the hand-gesture sensor includes a time-series data array of measured values from the at least one sensor sampled at a defined sampling rate, and wherein at least one word or phrase or the text representation of the at least one word or phrase is determined based in part on an output of the at least one brain sensor; a display screen; a computer-readable memory storing a gesture database, wherein the gesture database stores data indicative of a plurality of unique time-series data arrays and defines a different word or phrase corresponding to each unique time-series data array of the plurality of time-series data arrays; and an electronic processor configured to receive data from the hand-gesture sensor indicative of one or more gestures performed by a user, determine, based on the data from the hand-gesture sensor, at least one word or phrase corresponding to the data from the hand-gesture sensor by identifying a unique time-series data array of the plurality of unique time-series data arrays stored on the computer-readable memory that most closely matches the data from the hand-gesture sensor, and output on the display screen a text representation of the at least one word or phrase, selectively operate in a training mode, output a prompt on the display screen, while operating in the training mode, instructing the user to perform a gesture corresponding to a particular word or phrase, receive the data from the hand-gesture sensor after outputting the prompt, and update the gesture database to define a new unique time-series data array corresponding to the particular word or phrase based on the data received from the hand-gesture sensor after outputting the prompt, output a prompt on the display screen instructing the user to perform an American Sign Language (ASL) gesture corresponding to the particular word or phrase, and train the gesture database by iteratively prompting the user to perform a different one of a series of ASL gestures while receiving data from the hand-gesture sensor after each prompt. 